The techical knowlegde of the trainer about the subject so he can answer most of the questions.

Synchronoss Software Ireland Limited

课程: Docker and Kubernetes

The exercises

Van Tonilin Ramos - Temic Automotive Philippines

课程: Docker and Kubernetes

All

Eduardo Darroca Jr - Temic Automotive Philippines

课程: Docker and Kubernetes

It never felt like the pace was high but we still managed to cover many topics in only three days. I liked that Adriano did not stop the lecture to have us do labs. The style of trying out commands in the lab environment while watching and listening to the lecture was good in my opinion. Adriano was very flexible to answer questions that came up without getting distracted too much from the current topic.

Bolagsverket

课程: Docker and Kubernetes

所有

Mikael Larsson Österström - Bolagsverket

课程: Docker and Kubernetes

Machine Translated

The teacher has worked with the subject

Bolagsverket

课程: Docker and Kubernetes

The material in general. The trainer was very knowledgable.

MDA Systems Ltd.

课程: Kubernetes Advanced

培训师解释主题的方式以及使概念变得非常简单的人员。

Faten AlDawish - TAMKEEN TECHNOLOGIES COMPANY

课程: Docker和Kubernetes：构建和缩放容器化应用程序

Machine Translated

实验室和疑惑澄清

venkata velpuri - Charter Communications INC

课程: Kubernetes：基础到高级

Machine Translated

操作培训。

Charter Communications INC

课程: Kubernetes：基础到高级

Machine Translated

在培训期间，没有感到无聊的地方。

Charter Communications INC

课程: Kubernetes：基础到高级

Machine Translated

Fantastic delivery of the training, really enjoyable and all round good vibe throughout

Chris Brant - Hewlett Packard Enterprise

课程: Docker and Kubernetes

我喜欢在演讲中补充理论的例子。

Miłosz Gałązka - LPP S.A.

课程: OpenShift for Administrators

Machine Translated

We did small examples. I have seen a lot of huge config files and they always looked overwhelming. It was really nice to see small examples!

Marcus Nordberg - TriOptima AB

课程: Docker and Kubernetes

Kamil's attitude and knowledge. Very friendly, highly knowledgeable about the subject, great listener.

TriOptima AB

课程: Docker and Kubernetes

Kamil's expertise and patience while guiding me through some complex topics. His ability to spontaneously answer some difficult questions that I had about the material. I'd give him an A++.

课程: Docker and Kubernetes

the intro and the structure of the slides

Abraxas Informatik AG

课程: Rancher：管理你的Docker容器

大量的练习和设置练习。

Atos Poland Global Services spółka z ograniczoną odpowiedzialnością

课程: Docker and Kubernetes

Machine Translated

Deep expertise of the faculty

课程: Creating a Service Mesh with Istio and Kubernetes

Kamil's expertise and patience while guiding me through some complex topics. His ability to spontaneously answer some difficult questions that I had about the material. I'd give him an A++.

Kubeflow is a framework for running Machine Learning workloads on Kubernetes. TensorFlow is a machine learning library and Kubernetes is an orchestration platform for managing containerized applications.

This instructor-led, live training (onsite or remote) is aimed at engineers who wish to deploy Machine Learning workloads to an AWS EC2 server.

By the end of this training, participants will be able to:

- Install and configure Kubernetes, Kubeflow and other needed software on AWS.- Use EKS (Elastic Kubernetes Service) to simplify the work of initializing a Kubernetes cluster on AWS.- Create and deploy a Kubernetes pipeline for automating and managing ML models in production.- Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.- Leverage other AWS managed services to extend an ML application.

Format of the Course

- Interactive lecture and discussion.- Lots of exercises and practice.- Hands-on implementation in a live-lab environment.

Course Customization Options

- To request a customized training for this course, please contact us to arrange.

Kubeflow is a framework for running Machine Learning workloads on Kubernetes. TensorFlow is one of the most popular machine learning libraries. Kubernetes is an orchestration platform for managing containerized applications.

This instructor-led, live training (onsite or remote) is aimed at engineers who wish to deploy Machine Learning workloads to Azure cloud.

By the end of this training, participants will be able to:

- Install and configure Kubernetes, Kubeflow and other needed software on Azure.- Use Azure Kubernetes Service (AKS) to simplify the work of initializing a Kubernetes cluster on Azure.- Create and deploy a Kubernetes pipeline for automating and managing ML models in production.- Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.- Leverage other AWS managed services to extend an ML application.

Format of the Course

- Interactive lecture and discussion.- Lots of exercises and practice.- Hands-on implementation in a live-lab environment.

Course Customization Options

- To request a customized training for this course, please contact us to arrange.

Kubeflow is a framework for running Machine Learning workloads on Kubernetes. TensorFlow is one of the most popular machine learning libraries. Kubernetes is an orchestration platform for managing containerized applications.

This instructor-led, live training (onsite or remote) is aimed at engineers who wish to deploy Machine Learning workloads to Google Cloud Platform (GCP).

By the end of this training, participants will be able to:

- Install and configure Kubernetes, Kubeflow and other needed software on GCP and GKE.- Use GKE (Kubernetes Kubernetes Engine) to simplify the work of initializing a Kubernetes cluster on GCP.- Create and deploy a Kubernetes pipeline for automating and managing ML models in production.- Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.- Leverage other GCP services to extend an ML application.

Format of the Course

- Interactive lecture and discussion.- Lots of exercises and practice.- Hands-on implementation in a live-lab environment.

Course Customization Options

- To request a customized training for this course, please contact us to arrange.

Kubeflow is a framework for running Machine Learning workloads on Kubernetes. TensorFlow is one of the most popular machine learning libraries. Kubernetes is an orchestration platform for managing containerized applications.

This instructor-led, live training (onsite or remote) is aimed at engineers who wish to deploy Machine Learning workloads to IBM Cloud Kubernetes Service (IKS).

By the end of this training, participants will be able to:

- Install and configure Kubernetes, Kubeflow and other needed software on IBM Cloud Kubernetes Service (IKS).- Use IKS to simplify the work of initializing a Kubernetes cluster on IBM Cloud.- Create and deploy a Kubernetes pipeline for automating and managing ML models in production.- Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.- Leverage other IBM Cloud services to extend an ML application.

Format of the Course

- Interactive lecture and discussion.- Lots of exercises and practice.- Hands-on implementation in a live-lab environment.

Course Customization Options

- To request a customized training for this course, please contact us to arrange.

Kubeflow is a framework for running Machine Learning workloads on Kubernetes. TensorFlow is one of the most popular machine learning libraries. Kubernetes is an orchestration platform for managing containerized applications. OpenShift is an cloud application development platform that uses Docker containers, orchestrated and managed by Kubernetes, on a foundation of Red Hat Enterprise Linux.

This instructor-led, live training (onsite or remote) is aimed at engineers who wish to deploy Machine Learning workloads to an OpenShift on-premise or hybrid cloud.

- By the end of this training, participants will be able to:- Install and configure Kubernetes and Kubeflow on an OpenShift cluster.- Use OpenShift to simplify the work of initializing a Kubernetes cluster.- Create and deploy a Kubernetes pipeline for automating and managing ML models in production.- Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.- Call public cloud services (e.g., AWS services) from within OpenShift to extend an ML application.

Format of the Course

- Interactive lecture and discussion.- Lots of exercises and practice.- Hands-on implementation in a live-lab environment.

Course Customization Options

- To request a customized training for this course, please contact us to arrange.

Kubeflow is a toolkit for making Machine Learning (ML) on Kubernetes easy, portable and scalable.

This instructor-led, live training (onsite or remote) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes.

By the end of this training, participants will be able to:

- Install and configure Kubeflow on premise and in the cloud.- Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.- Run entire machine learning pipelines on diverse architectures and cloud environments.- Using Kubeflow to spawn and manage Jupyter notebooks.- Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

Format of the Course

- Interactive lecture and discussion.- Lots of exercises and practice.- Hands-on implementation in a live-lab environment.

Course Customization Options

- To request a customized training for this course, please contact us to arrange.- To learn more about Kubeflow, please visit: https://github.com/kubeflow/kubeflow